Senior Applied Computer Vision Engineer

Role Overview

• Develop and improve computer vision models for sports video, including player and ball detection, tracking, event recognition, and identity association.
• Build and improve camera calibration, homography, and field-registration solutions that map image coordinates into normalized field coordinates.
• Analyze existing computer vision pipelines, establish baselines, identify weak links, and recommend practical improvements.
• Improve tracking robustness across different stadiums, camera placements, broadcast styles, video qualities, and environmental conditions.
• Design experiments covering data acquisition, dataset creation, augmentation, model training, fine-tuning, evaluation, and deployment readiness.
• Analyze failure modes and implement improvements that increase accuracy, reliability, scalability, and robustness.
• Adapt existing models and pipelines to support new sports, leagues, camera configurations, and video sources.
• Partner with data teams on labeling workflows, dataset quality, validation processes, and human-in-the-loop improvement cycles.
• Work closely with software, platform, and DevOps engineers to deploy computer vision models and pipelines into production environments.
• Improve inference performance, scalability, monitoring, and operational reliability.
• Establish evaluation metrics, testing processes, and quality controls to ensure model performance remains consistent over time.
• Lead initiatives end-to-end, from early technical discovery and prototyping through production deployment and ongoing improvement.
• Contribute to system design decisions that integrate computer vision, machine learning, backend services, operations, and client workflows.
• Communicate technical tradeoffs clearly with internal teams, client stakeholders, and engineering leadership.

Requirements

• Strong hands-on experience building and improving production-grade computer vision systems.
• Proficiency with Python and modern machine learning frameworks such as PyTorch.
• Experience with video-based computer vision problems, including object detection, multi-object tracking, event recognition, identity association, or video analytics.
• Strong working knowledge of geometric computer vision, including camera calibration, homography estimation, projective geometry, and mapping image-space detections to real-world 2D or 3D coordinates.
• Experience designing or improving tracking systems that handle occlusions, object interactions, identity preservation, noisy detections, and missing information.
• Experience evaluating model performance, identifying failure modes, and implementing practical improvements.
• Experience adapting models to challenging real-world data where video quality, camera angles, camera placement, and environmental conditions vary significantly.
• Experience with transfer learning, domain adaptation, data augmentation, and fine-tuning models on domain-specific datasets.
• Strong software engineering fundamentals and the ability to write clean, maintainable, production-quality code.
• Ability to work independently, prioritize effectively, and drive technical initiatives to completion.
• Strong communication skills and the ability to collaborate directly with clients and cross-functional engineering teams.

Tech Stack

• Python
• PyTorch

Benefits

• Competitive compensation with benefits, paid vacation, and sick leave.
• The opportunity to work with a globally diverse team of top engineering talent on the industry’s toughest engineering challenges.
• Ultra-flexible working conditions – we provide a generous office equipment allowance so you can work from home, we can also provide you with a desk at an office/coworking facility near you, or use both. No business travel necessary.
• An enjoyable, start-up work environment, with excellent opportunities for professional growth and development.
• Flexible working hours – as a remote-first company, our focus has always been on getting the job done well, not when or where it gets done.